Patterns of influenza vaccination coverage in the United States from 2009 to 2015

Accepted Manuscript Title: Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015 Authors: Alice P.Y. Chiu, Jonathan Dushoff, Duo Yu, Daihai He PII: DOI: Reference:

S1201-9712(17)30260-6 https://doi.org/10.1016/j.ijid.2017.10.004 IJID 3058

To appear in:

International Journal of Infectious Diseases

Received date: Revised date: Accepted date:

22-8-2017 4-10-2017 6-10-2017

Please cite this article as: Chiu Alice PY, Dushoff Jonathan, Yu Duo, He Daihai.Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015.International Journal of Infectious Diseases https://doi.org/10.1016/j.ijid.2017.10.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015

Alice P.Y. Chiu1, Jonathan Dushoff2,3, Duo Yu4, Daihai He1,*

1

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong (SAR)

China 2

Department of Biology, McMaster University, Hamilton, ON, Canada

3

M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON,

Canada 4

Department of Biostatistics, School of Public Health, University of Texas Health Science Center

at Houston, United States

*Correspondence to: [email protected] TU804, Yip Kit Chuen Building, Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong (SAR) China

Highlights ● State-level patterns of influenza coverage rates are investigated. ● Influenza vaccination rates are positively associated with health-care access . ● This represents a potential population-level advantage of expanding health-care access.

Abstract Background 1

Globally, influenza is a major cause of morbidity, hospitalization and mortality. Influenza vaccination has shown substantial protective effectiveness in the United States. Methods We investigated state-level patterns of coverage rates of seasonal and pandemic influenza vaccination, among the overall population (six months or older) in the U.S. and specifically among children (aged between 6 months and 17 years) and the elderly (aged 65 years or older), from 2009/10 to 2014/15, and associations with ecological factors. We obtained state-level influenza vaccination rates from national surveys, and state-level socio-demographic and health data from a variety of sources. We employed a retrospective ecological study design, and used both linear models and linear mixed-effect models to determine the levels of ecological association of the state-level vaccinations rates with these factors, both with and without region as a factor for the three populations. Results and Conclusions Health-care access has a robust, positive association with state-level vaccination rates across all populations and models. This highlights a potential population-level advantage of expanding health-care access. We also found that prevalence of asthma in adults is negatively associated with mean influenza vaccination rates in the elderly populations.

Keywords: influenza, vaccination coverage.

Introduction Globally, influenza is the cause of three to five million cases of severe illness and 250,000 to 500,000 deaths annually1. Influenza vaccine coverage rates in the United States are the highest among the Americas2. The US Healthy People 2020 initiative set a target of 70% vaccination coverage of both children and adults by 20203. Vaccine effectiveness against seasonal influenza A and B virus infection and pandemic influenza (A(H1N1)pdm09) has demonstrated moderate effectiveness4.

2

Previous studies have investigated individual-level correlates of seasonal and pandemic influenza vaccination rates in the US. It was found that younger age5,6,7, lower levels of education5,7, lower household income5,8, urbanicity6, having a history of chronic conditions or poor physical health5,7, lack of health insurance7,8, and smoking7 were factors associated with lower coverage. In addition, non-Hispanic Black race was associated with lower rates of pandemic influenza vaccination9. However, whether these factors could explain state-level variations has not been investigated. Also, regional variations in vaccination rates have not been considered. In the US, the Center for Diseases Control and Prevention (CDC) recommended seasonal influenza vaccination to all persons aged 6 months or older in 2010/11 following the 2009 influenza A (H1N1) pandemic10. In this study, our aim is to identify how socio-demographic and health-related factors are ecologically associated with the mean state-level rates of influenza vaccination coverage among the overall population (i.e. persons aged six months or above) from 2009/10 to 2014/15. Our secondary aim is to compare the association of these factors in the presence or absence of region as a factor. In addition to our main analysis in the whole population, we studied these associations separately in the elderly (aged 65 or above) and children (aged 6 months to 18 years) sub-populations. Methods Study Population and Setting Our study population (“overall population") consisted of all persons aged six months or older from 50 states and the District of Columbia in the U.S. from 2009/10 to 2014/15 who were eligible for influenza vaccination. We also studied the elderly and children sub-populations within the overall population. All data were obtained from publicly available sources. Study Design We adopted a retrospective ecological study design to assess the overall socio-demographic, health-related and regional correlates with the mean state-level vaccination rates across the time period. This study design does not permit the analyses of individual-level associations. However, it is appropriate for studying the state-level vaccination rates and their potential associations with these factors.

3

Influenza Vaccination Coverage Data We used published data from Behavioral Risk Factor Surveillance System (BRFSS)

11

and

National Immunization Survey (NIS)12. These sources give influenza coverage rates for the 50 states plus the District of Columbia for each influenza seasonal year (from June through May) for the period from 2009/10 to 2014/15. BRFSS's study population included adults aged 18 year or above whereas NIS study population covered children aged 6 months to 17 years old. For 2009/10, due to the H1N1 pandemic, they report coverage for: seasonal vaccination, pandemic H1N1 vaccination, or at least one influenza vaccination. We used the last value. These vaccination rates ranged from 30% to 60%. We also computed the mean rates based on their annual rates from 2009/10 to 2014/15. These mean state-level rates are then used for further analyses. Region We aggregated the 50 states and the District of Columbia into ten regions according to the US Human and Health Services (HHS) classification13. Socio-demographic and Health-related Data To investigate factors associated with vaccination rates, we searched publicly available sources for socio-demographic and health-related data. Specifically, we collected secondary data for the following state-level variables in which their data definitions and data sources are described in more details below. We chose socio-demographic and health variables considered relevant based on previous literature5,6,7,8,9 Health Care Access We obtained estimates from the CDC's Behavioral Risk Factor Surveillance System (BRFSS) Surveys in 2013. Health care access is defined as having any kind of health insurance, prepaid plans, government plans or Indian Health Service14. Population Proportion of Children and Elderly We obtained population estimates from the US Census Bureau in 2013 to determine the proportion of children and elderly in each state 15. Per capita Personal Income Level 4

We also obtained the per capita income level in 2013 from the US Bureau of Economic Analysis in each state 16. Educational Attainment Data about educational attainment is obtained from the US Census Bureau in 2013. It is defined as the proportion of population who had attained college degree or above17. Racial or Ethnic Origin We defined race and ethnic origin into two variables: Hispanic and Black. We classified persons into Hispanic or non-Hispanic origin. We also defined persons as Black or non-Black (i.e. White, Asian American, Native American or others, including persons who are multi-racial). Prevalence of Asthma We estimated prevalence of asthma among the adult population using responded from the 2013 BRFSS survey18. Missing Data There are missing data in the 2013/14 influenza vaccination coverage for two states. Since vaccination levels are relatively constant from year to year, we simply calculate mean coverage for those two states using the remaining years. We filled the “N/A" entry of “Black" for Wyoming using 1 minus the proportions of White, Hispanic, Others, and yield 0.009845. Statistical Analyses

We fit linear models to test the effects of socio-demographic and health variables for the overall, children and elderly populations (thus fitting a total of three models). We then added region as a random factor to each of these models, to control for spatial and cultural patterns at that scale (thus fitting three additional linear mixed-effect models). We standardised all variables to mean zero and unit standard deviation before performing analyses. We conducted all statistical analyses using R (version 3.2.2). Results

5

Figure 1 shows patterns of influenza vaccination coverage across the US. Vaccination is lower in the Northwestern and Western regions, and higher in the Mid-Western and Northeastern regions. There were also large state-level variations within Mid-western region. We show estimates from our regression models in Figures 2-4, and Supplementary Tables 1-3. All continuous regression variables are standardized to unit standard deviation. Thick lines show 50% confidence intervals, and thin lines show 95% confidence intervals. Figure 2 shows our model results for the overall population (with and without region as a random factor). Health care access has a significant positive association with influenza vaccination rates at 5% level; it is the only one of our factors that shows a significant association. When comparing the two panels, all of the regression estimates are similar, and there were no changes in sign. Figure 3 shows our model results for the elderly population. Health care access is positively associated with coverage as before. Additionally, prevalence of adult asthma is negatively associated with vaccination rates in the elderly. No other factors show statistical significance. When comparing the two panels, all of the regression estimates are similar, and there were no changes in sign. Figure 4 shows our model results for the children population. Again, health care access is positively associated with coverage. No other factors show statistical significance. When comparing the two panels, all of the regression estimates are similar, and there were no changes in sign. Data and model are shown in the supporting Files 1 and 2. Discussion Main findings of this study Health care access shows a consistent, positive and significant association with influenza vaccination coverage at the state level, across all of our population groups, and whether or not region is accounted for. We also found that a significant, negative association between the prevalence of asthma in adult vaccination coverage in the elderly population. What is already known on this topic?

6

Our results were consistent with Fox and Shaw8 who showed that at individual level, adults with health care access are more likely to receive influenza vaccination than those without such access. Bish et al conducted a systematic review and found that being male, of older age, being an ethnic minority are demographic factors associated with uptake of vaccination against pandemic influenza19. Brien et al conducted another systematic review and found that being male, younger age, higher educational level, being a doctor, being in a priority group for influenza vaccination, receiving a seasonal vaccination in the past, believing in the safety and efficacy of the vaccine, and obtaining vaccine-related information from medical sources, are associated with uptake of pandemic influenza vaccination20. These two systematic reviews considered pandemic influenza vaccination only, and they covered individual-level studies from many countries have varying levels of vaccination coverages. In contrast, our ecological study did not find evidence that educational attainment has a positive effect at the population level. Similar studies investigating individual-level factors associated with influenza vaccination rates were conducted in other countries. A Korean study found that among adults, older age, lower educational attainment, lower personal income, health behaviour, being a non-smoker or having a chronic health condition21. A similar study among middle- and older-aged Australian adults found that having a medical indication recommended for influenza vaccination suggested high uptake among those who have free access to influenza vaccine22. Previous research studied the racial disparities in influenza vaccination coverage in the U.S. from 2007/08 through 2011/12. Their results showed that at individual level, vaccination rates among the adult subgroups were lower among Hispanics and non-Hispanic blacks, when compared to non-Hispanic whites23. Uscher-Pines et al. found that Hispanics and non-Hispanic whites have similar vaccination rates for pandemic influenza, and they suggested this could be due to the Mexican origin of the pandemic that leads to heightened awareness among Hispanics for pandemic influenza vaccination9. They also found that seasonal vaccination in 2009/10 was significantly higher among non-Hispanic whites than non-Hispanic blacks and Hispanics9. At the state level, we did not find a consistent pattern, nor any statistically significant evidence for associations between proportion Hispanic and influenza coverage. However, our ecological findings showed that prevalence of asthma in adults was significantly and negatively associated with influenza coverage in the elderly. A South Korean population-based 7

cross-sectional study showed that older age were associated with increased vaccination rates among adult asthma patients24. Strengths of this study include a comparison of ecological factors across six different models, covering overall, elderly and children populations and taking region into account. We were also able to investigate several potentially relevant socio-demographic and health-related factors. Limitations of this study The main study limitation is that it is a state-level ecological study, so we cannot make direct conclusions about causation, nor examine effects at the individual level. Further, although we have studied a number of potentially relevant factors, other factors might be important at a relevant scale -- for example, influenza epidemic levels, mass media reports on influenza, reimbursement rates for influenza vaccination, and vaccine availability25. What this study adds? This study shows a population-level effect of health-care access on influenza vaccination level, while controlling for other covariates. The effect is robust to controlling for regional variation as a random effect. We additionally find that the amount of variance explained by region as a random effect is comparable to that seems to be explained by covariates; future studies could employ more formal techniques to investigate geospatial clustering of vaccination. Conclusions Our analysis of state level patterns found that health care access is positively associated with mean influenza vaccination rates in overall, elderly and children populations, both with and without region as a factor. Thus, reducing health care coverage could have a negative impact on influenza vaccination rates. We could also look at whether the burdens of influenza-related illnesses and deaths of the earlier years are correlated with the influenza vaccination rates of the current year. We also found that prevalence of asthma among adults is negatively and significantly associated with vaccination coverage in the elderly population. This information may be helpful to identify target populations and to tailor future influenza vaccination campaigns according to these variations, so as to achieve the Healthy People influenza vaccination goals in 2020 for both children and adults26. 8

Author contributions statement D.H., A.C., J.D. and D.Y. conceived the experiment(s) and conducted the experiment(s), D.H., A.C., J.D. analysed the results. All authors reviewed the manuscript.

Competing Interests The authors declare that they have no competing interests.

Acknowledgements D.H. was partially supported by the Early Career Scheme from Hong Kong Research Grants Council (PolyU 251001/14M)

9

References 1 World Health Organization. Influenza (Seasonal). Geneva: Media Center; 2016. Available online: http://www.who.int/mediacentre/factsheets/fs211/en/ (accessed on 17 August 2017). 2 Palache A, Oriol-Mathieu V, Abelin A, Music T, Influenza Vaccine Supply task force (IFPMA IVS). Seasonal influenza vaccine dose distribution in 157 countries (2004-2011). Vaccine. 2014;32:6369-76. 3 National Vaccine Advisory Committee. Strategies to achieve the healthy people 2020 annual influenza vaccine coverage goal for health-care personnel: recommendations from the national vaccine advisory committee. Public Health Rep. 2013;128:7-25. 4 Flannery B, et al. Interim estimates of 2013-14 seasonal influenza vaccine effectiveness – United States, February 2014. MMWR Morb Mortal Wkly Rep. 2014;63:137-42. 5 Linn ST, Guralnik JM, Patel KV. Disparities in influenza vaccine coverage in the United States, 2008. J Am Geriatr Soc. 2010;58:1333-40. 6 Galarce EM, Minsky S, Viswanath K. Socioeconomic status, demographics, beliefs and A(H1N1) vaccine uptake in the United States. Vaccine. 2011;29:5284-9. 7 Takayama M, Wetmore CM, Mokdad AH. Characteristics associated with the uptake of influenza vaccination among adults in the United States. Prev Med. 2012;54:358-62. 8 Fox JB, Shaw FE; Office of Health System Collaboration, Office of the Associate Director for Policy, CDC. Relationship of income and health care coverage to receipt of recommended clinical preventive services by adults - United States, 2011-2012. MMWR Morb Mortal Wkly Rep. 2014;63:666-70. 9 Uscher-Pines L, Maurer J, Harris KM. Racial and ethnic disparities in uptake and location of vaccination for 2009-H1N1 and seasonal influenza. Am J Public Health. 2011;101:1252-5. 10 Centers for Disease Control and Prevention (CDC). Prevention and control of seasonal influenza with vaccines. Recommendations of the Advisory Committee on Immunization Practices--United States, 2013-2014. MMWR Recomm Rep. 2013;62:1-43. 11 Xu F, et al. Surveillance for certain health behaviors among states and selected local areas-Behavioral Risk Factor Surveillance System, United States, 2011. MMWR Surveill Summ. 2014;63:1-149.

10

12 Center for Disease Control and Prevention (CDC). Datasets and Related Documentation for the National Immunization Survey, 2010-2014. United States; 30 March 2015. Available online: http://www.cdc.gov/nchs/nis/data_files.htm (accessed on 17 August 2017). 13 US Department of Health and Human Services. Regional Offices. https://www.hhs.gov/about/agencies/iea/regional-offices/index.html (accessed on 15 September 2017).

US Census Bureau. Census Divisions and Census Regions - Census.gov; 2015. Available online: https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html (accessed on 17 August 2017). 14 Behavioral Risk Factor Surveillance System. Prevalence and Trends Data. Health Care Access/Coverage - 2013. United States; 2013. Available online: http://apps.nccd.cdc.gov/brfss/list.asp?cat=HC&yr=2013&qkey=8021&state=All (accessed on 17 August 2017). 15 U.S. Census Bureau. Annual Social and Economic Supplement of the Current Population Survey. United States; 2013. Available online: https://www.census.gov/prod/techdoc/cps/cpsmar13.pdf (accessed on 17 August 2017). 16 Bureau of Economic Analysis. Regional Economic Accounts: Download. United States; 2013. Available online: http://www.bea.gov/regional/downloadzip.cfm (accessed on 17 August 2017). 17 U.S. Census Bureau. Educational Attainment - U.S. Census Bureau - Census.gov [Internet]. United States; 2013. Available online: http://www.census.gov/hhes/socdemo/education/ (accessed on 17 August 2017). 18 Asthma Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Prevalence and Trends Data. Asthma, 2013 United States; 2015. Available online: http://www.cdc.gov/asthma/brfss/2013/ (accessed on 17 August 2017). 19 Bish A, et al. Factors associated with uptake of vaccination against pandemic influenza: a systematic review. Vaccine. 2011;29:6472-84. 20 Brien S, Kwong JC, Buckeridge DL. The determinants of 2009 pandemic A/H1N1 influenza vaccination: a systematic review. Vaccine. 2012;30:1255-64.

11

21 Yang HJ, Cho SI. Influenza vaccination coverage among adults in Korea: 2008-2009 to 20112012 seasons. Int J Environ Res Public Health. 2014;11:12162-73. 22 Dyda A, et al. Factors associated with influenza vaccination in middle and older aged Australian adults according to eligibility for the national vaccination program. Vaccine. 2015;33:3299-305. 2323 Lu PJ, O'Halloran A, Bryan L, et al. Trends in racial/ethnic disparities in influenza vaccination coverage among adults during the 2007-08 through 2011-12 seasons. Am J Infect Control. 2014;42:763-9. 24 Chung JH, Kim TH, Han CH. Factors influencing influenza vaccination among South Korean adult asthma patients: A nationwide population-based cross-sectional study. J Asthma. 2017; May 17:1-6. 25 Yoo BK. How to improve influenza vaccination rates in the U.S. J Prev Med Public Health. 2011;44:141-8. 26 Healthy People 2020. Topics and objectives. Immunization and infectious diseases. Available online: https://www.healthypeople.gov/2020/topics-objectives/topic/immunization-andinfectious-diseases (accessed on 17 August 2017).

12

Fig. 1 Mean influenza vaccination rates of 50 States and DC. Higher-rate states are colored in darker gray, and lower-rate states are colored in lighter gray. Hawaii and Alaska are shifted, and Alaska is scaled by 1/3.

13

Fig. 2 Estimates for the overall population without (left) or with (right) region as a factor.

14

Fig. 3 Estimates for elderly without (left) or with (right) region factor.

15

Fig. 4 Estimates for children without (left) or with (right) region as a factor.

16

Table 1. Estimates for the overall population with or without region as a factor.

(Intercept) Black Hispanic Origin Population Proportion of Children Population Proportion of Elderly Prevalence of Asthma in Adults Educational Attainment Per capita Personal Income Level Health Care Access Region Standard Deviation

Linear model Estimate Standard error 6.217e-17 0.115 -0.039 0.14 0.031 0.147

t value

p-value

0.000 -0.277 0.212

1.000 0.783 0.833

Linear Mixed Effects Model Estimate Standard t value error -0.023 0.180 -0.131 -0.028 0.154 -0.180 0.063 0.158 0.400

0.077

0.215

0.358

0.722

0.009

0.208

0.043

0.27

0.201

1.345

0.186

0.144

0.187

0.770

-0.208

0.155

-1.345

0.186

-0.240

0.152

-1.577

-0.176

0.175

-1.010

0.318

-0.080

0.173

-0.459

0.206

0.239

0.864

0.392

-0.022

0.250

-0.087

0.651

0.201

3.235

0.002

0.795

0.202

3.926

0.460

17

Table 2. Estimates for the elderly population with or without region as a factor.

(Intercept) Black Hispanic Origin Population Proportion of Children Population Proportion of Elderly Prevalence of Asthma in Adults Educational Attainment Per capita Personal Income Level Health Care Access Region Standard deviation

Linear model Estimate Standard error -9.286e-16 0.123 0.02 0.15 -0.072 0.16

t value

p-value

0.000 0.132 -0.456

1.000 0.896 0.651

Linear Mixed Effects Model Estimate Standard t value error -0.009 0.183 -0.046 0.017 0.165 0.106 -0.034 0.170 -0.202

-0.221

0.23

-0.959

0.343

-0.267

0.226

-1.183

0.134

0.215

0.623

0.537

0.015

0.204

0.074

-0.452

0.166

-2.723

0.009

-0.423

0.165

-2.564

-0.163

0.187

-0.872

0.388

-0.093

0.187

-0.498

-0.171

0.256

-0.669

0.507

-0.354

0.269

-1.315

0.52

0.215

2.414

0.020

0.630

0.219

2.879

0.3748

18

Table 3. Estimates for the children population with or without region as a factor.

(Intercept) Black Hispanic Origin Population Proportion of Children Population Proportion of Elderly Prevalence of Asthma in Adults Educational Attainment Per capita Personal Income Level Health Care Access Region Standard Deviation

Linear model Estimate Standard error 1.120e-16 0.104 0.089 0.126 0.363 0.133

t value

p-value

0.000 0.706 2.729

1.000 0.484 0.009

Linear Mixed Effects Model Estimate Standard t value error -0.014 0.153 -0.088 0.117 0.139 0.845 0.349 0.143 2.433

0.09

0.194

0.463

0.646

0.066

0.192

0.343

0.252

0.181

1.388

0.172

0.193

0.173

1.116

0.071

0.14

0.510

0.613

0.011

0.140

0.081

-0.286

0.158

-1.814

0.077

-0.200

0.159

-1.262

0.401

0.216

1.857

0.070

0.248

0.228

1.089

0.578

0.182

3.178

0.003

0.675

0.185

3.644

0.4512

19